High-Performance Computing Engineer

About the role

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Position: High Performance Computing Engineer, 100% Remote (U.S.), Full-time, Direct W2. Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply; the company is unable to sponsor new H-1B visa petitions for this position.

Responsibilities

• Design and optimize compute-intensive workloads on modern accelerator hardware.
• Extract maximum performance from GPU platforms for AI training, inference, scientific computing, and high‑throughput data processing workloads.
• Work closely with cross‑functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well‑engineered solutions.
• Raise the bar through code review, design review, and mentorship of more junior engineers.
• Ship meaningful work that holds up well in production.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
• Six or more years of experience in GPU programming and performance engineering.
• Deep expertise in CUDA C/C++ and GPU programming models.
• Strong understanding of modern GPU architectures, memory hierarchies, and execution models.
• Hands‑on experience profiling and optimizing GPU workloads in production.
• Familiarity with NCCL, MPI, and high‑performance interconnect technologies.
• Experience integrating custom kernels into ML frameworks.
• Strong C++ skills and familiarity with modern systems programming practices.
• Solid grounding in linear algebra and numerical methods.
• Strong communication and collaboration skills with research and engineering teams.

Preferred Qualifications

• Experience with Triton, CUTLASS, or other GPU kernel authoring frameworks.
• Familiarity with TensorRT, FasterTransformer, or vLLM internals.
• Exposure to compiler infrastructure such as LLVM or MLIR.
• Open‑source contributions to GPU or ML performance libraries.
• Experience with large‑scale distributed training infrastructure.

Pay

$130,000–$150,000 annually.